Analysis of Machine Learning Models for Stock Market Prediction

With financial analysts and top institutions around the world utilising its power to derive sophisticated insights, machine learning has quickly become a cornerstone in the world of modern stock trading. The widespread use of this technology highlights the sector’s constant quest for higher predictive accuracy. Despite this, the stock market’s inherent volatility—which is influenced by a confluence of factors including human emotions, global events, economic shifts, and unforeseen circumstances—remains complicated, making the search for the ideal predictive model difficult. The research delves into this complexity and offers a thorough analysis of the various machine learning techniques used in stock market forecasting. Each algorithm’s effectiveness has been carefully evaluated using a battery of performance metrics on datasets that include stock prices from various companies, ranging from conventional approaches to sophisticated deep learning techniques. The goal is to identify the most accurate predictive tool, serving as a benchmark for subsequent financial forecasting efforts. It was concluded that LSTM performs the best out of all the proposed methods.